Port Automation Research Articles

Enhancing Efficiency through Multi-Modal Logistics and Port Automation: A Case Study of Paradip Port

Enhancing Efficiency through Multi-Modal Logistics and Port Automation: A Case Study of Paradip Port

Distributed Energy Dispatch for Geo-Data Centers Port Microgrid

With the development of port automation and artificial intelligence, coordination with multi-geographic data centers (Geo-DCs) has become a viable solution to address the issue of limited port computing resources. This study proposes a distributed energy dispatch method for the port microgrid coordinated with Geo-DCs (Geo-DCPM), aimed at reducing port carbon emissions and operational costs. Consider the single point of failure problem and high construction costs of centralized data centers. Geo-DCs are first introduced to solve the problem of insufficient computing resources in ports. An energy consumption calculation model for Geo-DCs is established, considering the data load delay constraint and the data space transfer constraint caused by specific delay-sensitive loads in the port microgrid. Then, an energy dispatch model (EDM) is constructed for the Geo-DCPM, taking into account carbon capture costs. Moreover, based on mixed-integer linear programming, a distributed algorithm is proposed to solve the EDM problem. Finally, the simulation results verify the effectiveness of the proposed method. Compared with the centralized algorithm, the packet loss rate of the distributed algorithm combined with Geo-DCs is significantly lower, reduced by about 70%.

Algorithms in the Margins: Organized Community Resistance to Port Automation in the Los Angeles Harbor Area

Public deliberations on artificial intelligence and machine learning (AI/ML) provoke strong interest in automation, or the perceived displacement of human labor due to general technological advances. Social science scholarship on well-compensated technology workers and a growing global underclass suggests automation remains largely a myth, with emerging technologies generating new forms of computational and emotional work. But little research has examined automation from the vantage point of community members at perceived greatest risk of displacement, such as those sustained by generations of logistics labor and physically located in racialized, underserved areas. In the United States and around the globe, logistics work is directly targeted by “automation engineers” to reduce labor costs on behalf of industry interests, posing a severe threat to port workers and their respective communities. This article examines organized community resistance to automation within the Los Angeles Harbor Area, drawing on four public hearings following the Port of LA’s planned integration of autonomous vehicles for the movement of cargo (i.e., automated straddle carriers). We find community mobilizations widely sought to enlarge public consideration of AI/ML to encompass matters of societal well-being and the collective good. These include shifting from the legality of AI integration to underscore public morality in light of anticipated harm, in addition to emphasizing the invisible social arrangements surrounding the Port and nearby community life. Our research reveals how lay people may reassert their own importance when confronted by technoscientific injustice, such as by reframing the future of work toward an insistence on “the future of our communities.”

Faster RCNN mixed-integer optimization with weighted cost function for container detection in port automation

The development of port automation requires sensors to detect container movement. Vision sensors have recently received considerable attention and are being developed as AI advances, leading to various container motion detection methods. Faster-RCNN is a detection method that performs better precision and recall than other methods. Nonetheless, the detectors are set using the Faster-RCNN default parameters. It is of interest to optimized its parameters for producing more accurate detectors for container detection tasks. Faster RCNN requires mixed integer optimization for its continuous and integer parameters. Efficient Modified Particle Swarm Optimization (EMPSO) offers a method to optimize integer parameter by evolutionary updating the space of each candidate solution but has high possibility stuck in the local minima due to rapid growth of Gbest and Pbest space. This paper proposes two modifications to improve EMPSO that could adapt to the current global solution. Firstly, the non-Gbest and Pbest total position spaces are made adaptive to changes according to the Gbest and Pbest position spaces. Second, a weighted multiobjective optimization for Faster-RCNN is proposed based on minimum loss, average loss, and gradient of loss to give priority scale. The integer EMPSO with adaptive changes to Gbest and Pbest position space is first tested on nine non-linear standard test functions to validate its performance, the results show performance improvement in finding global minimum compared to EMPSO. This tested algorithm is then applied to optimize Faster-RCNN with the weighted cost function, which uses 1300 container images to train the model and then tested on four videos of moving containers at seaports. The results produce better performances regarding the speed and achieving the optimal solution. This technique causes better minimum losses, average losses, intersection over union, confidence score, precision, and accuracy than the results of the default parameters.

Socioeconomic Characteristics and Perspectives on Cargo Handling: A Study of Tin Can Island Port, Apapa, Nigeria

This research examines the socioeconomic and cargo handling characteristics of the Tin Can Island Port in Nigeria. The rise in global trade agreements has led to a cargo handling crisis in Nigeria. However, the perspectives of the socioeconomic characteristic have not yet been studied. The sampling method used was simple random sampling, with 100 questionnaires given to all the employees and consumers who use the terminal. However, the study was based on 93 respondents who completed the survey satisfactorily. In addition, it includes in-depth interviews with some of the port-terminal employees, a few customers, and neighborhood residents on the use of intelligent port systems. The data instruments' validity and reliability were checked with a Cronbach alpha reliability test of 0.80-0.89 and found to be accurate for all the parameters. The information was analyzed using descriptive, qualitative, and chi-square statistical methods. The study found that socioeconomic factors, especially the age of the cargo handling staff, impacted the port's capability. The level of port automation and the adoption of intelligent port systems are still relatively low, though remarkable since the reforms. The research shows a significant relationship between the perception of cargo traffic level and change in the cargo handling procedure at Tin Can Island Port. In addition, it reveals a substantial connection between policy adoption and cargo handling improvement at the port.

A Review of Progress and Applications of Automated Vacuum Mooring Systems

Compared with the traditional mooring system, the automated vacuum mooring system can meet the development needs of large-scale ship automation, port automation, and environmental protection. This review describes the latest research focuses, progress, applications, and future perspectives regarding the automated vacuum mooring system. First, the components, working principles, advantages, limits, and risks of the automated vacuum mooring system are discussed. Secondly, typical application cases of automated vacuum mooring systems are introduced, looking at two aspects of the ship-based system and shore-based system. Then, the routine maintenance of the automated vacuum mooring system is introduced. Finally, a discussion on the challenges and future perspectives of the automated vacuum mooring system is provided in this review. The advantages of an automated vacuum mooring system make it a potentially highly effective and economical option for a wider range of ship mooring than a traditional mooring system.

Investigating the Effects of Introducing Automated Straddle Carriers in Port Operations with a System Dynamics Model

Port automation has been in the forefront of maritime innovation in the last decade. On that front, Automated Straddle Carriers (ASCs) are increasingly used to move containers efficiently. However, the introduction of ASCs in port operations can be disruptive if not handled properly, especially since the field can face many uncertainties such as increased container trade. The purpose of the paper is to investigate whether the introduction of Automated Straddle Carriers in port operations can improve the overall efficiency. To achieve the objective, a System Dynamics model was developed and tested under different scenarios. The results indicate that the introduction of ASCs is accompanied by an increase in productivity of the vehicles which results in more TEUs serviced. One of the most interesting results of the various scenarios is that for all rates of incoming TEUs, berth productivity is superior when operations are performed with 5 ASCs than with 10 manned vehicles. Finally, another issue that port authorities should always have in mind is the need for coordination among the various sub-processes and optimization of the necessary vehicles in order to avoid under-utilization of resources.

Ultra-Reliable and Low-Latency 5G Systems for Port Automation

5G technology brings significant potential for enabling port automation, leading to improved flexibility, predictability, and safety in ports. In this article, we present main drivers and technologies required for port automation. We focus on future communication needs in ports and describe 5G communication networks with their ultra-reliable, low-latency, and high-capacity capabilities as a connectivity solution to forge the advanced technologies required to realize port automation. Extensive multi-cell simulations built on realistic measurement-based propagation models and prototype testing in lab and port environments demonstrate the 5G capabilities for ports.

A novel algorithm of ship structure modeling and target identification based on point cloud for automation in bulk cargo terminals

Achieving port automation of machinery at bulk terminals is a challenging problem due to the volatile operation environments and complexity of bulk loading compared to the situations in container terminals. In order to facilitate port automation, we present a method of hull modeling (reconstruction of hull’s structure) and operation target (cargo holds under loading) identification based on 3D point cloud collected by Laser Measurement System mounted on the ship loader. In the hull modeling algorithm, we incrementally register pairs of point clouds and reconstruct the 3D structure of bulk ship’s hull blocks in details through process of encoder data of the loader, FPFH feature matching and ICP algorithm. In the identification algorithm, we project real-time point clouds of the operation zone to spherical coordinate and transforms the 3D point clouds to 2D images for fast and reliable identification of operation target. Our method detects and complements four edges of the operation target through process of the 2D images and estimates both posture and size of operation target in the bulk terminal based on the complemented edges. Experimental trials show that our algorithm allows us to achieve the reconstruction of hull blocks and real-time identification of operation target with high accuracy and reliability.

Deep learning–assisted real-time container corner casting recognition

Intelligent automated crane systems are now an integral part of container port automation. Accurate corner casting detection boosts the performance of an automated crane system which ultimately automates ships loading and unloading. Existing techniques use various traditional laser-based and vision-based methods for corner casting detection. Challenging weather conditions, varying lighting conditions, light reflections from ground, and container rusting conditions are the main problems that affect the performance of automated cranes. From this line of research, we propose an end-to-end method that takes a low-quality video input and produces bounding boxes around corner castings by applying a recurrent neural network along with long short-term memory units. The expressive image features from GoogLeNet are used to produce intermediate image representations that are further tuned for our system. The proposed system uses back-propagation to allow joint tuning of all components. At least, four cameras are mounted on each crane and input stream is combined into a single image to reduce the computational cost. The proposed system outperforms all existing methods in terms of precision, recall, and F-measure. The proposed method is implemented in a real-time port and produces more than 98% accuracy in all conditions.

THE CO.TR.I.S SYSTEM: TOWARDS A SMARTER COASTAL TRANSPORT NETWORK FOR SMART ISLANDS

Abstract. The Coastal Transport Information System (Co.Tr.I.S) is a multifunction information system that is developed for the effective design of coastal transportation lines. The system incorporates several subsystems which include the models, tools, and techniques that support the design of improved coastal networks. Co.Tr.I.S main aim is to support any decision making process of the involved players (Ministry, Maritime companies, Local Authorities, Travel Agencies, Passengers, etc) regarding the improvement & the optimal use of a coastal transport network. Co.Tr.I.S data retrieval, analysis, visualization, network design & decision support can accelerate the very slow (currently annual in Aegean) rate of coastal transport network update/upgrade procedures, and, create smarter network implementations that may adapt online on the various demand or requirement changes or updates. Connections, transportation, mobility, as well as port automation are some of the key factors for the “smartification” of entire islands especially in an archipelago like the Aegean Sea. In this work, we present the network design optimization functionality of Co.Tr.I.S, the various optimization stages, the Genetic Algorithm (GA) implementations and its potential to propose a better network design based on each user preferences. A sample case study is given to show its smartness & adaptability to each user needs and, finally, a discussion follows on how it could be complemented by emerging smart technologies for smarter islands.

Employees Technology Usage Adaptation Impact on Companies Logistics Service Performance

The information technology (IT) capability of companies is one of the determinants of their competitive power. However, IT outputs depend on employees intentions to use them. As a technological investment Port automation systems are widely used in container terminals. Therefore behavioral intention in the usage of various IT applications is one of the important factors that may affect logistics service performance. This study aims to explore the employees' technology usage adaptation impact on the logistics service performance of ports. In this context, the behavioral intentions of employees who use port automation systems are investigated using the Technological Acceptance Model.

A FAST AUTOMATED VISION SYSTEM FOR CONTAINER CORNER CASTING RECOGNITION

With the development of container port automation, the automated vision systems for containers have been widely used in automated ports. This paper presents a rapid automated vision system for container corner casting recognition. The histograms of oriented gradients (HOG) descriptors are used to preprocess the image of the container and the vectors of HOG are then built. A trained support vector machine (SVM) classifier is applied to recognize the right corner casting of the container. At last, through symmetry, a flipping mirror algorithm is used for quick left corner casting recognition. The experimental results show this algorithm scans and detect the two corner castings of the container almost twice as fast as the traditional algorithms.

Ship Identification Algorithm Based on 3D Point Cloud for Automated Ship Loaders

Mi, C.; Shen, Y.; Mi, W., and Huang, Y., 2015. Ship identification algorithm based on 3D point cloud for automated ship loader.With the development of bulk port automation, the ship loader as the main quayside machine of bulk terminal is required for transformation from manual operation to automation. The ship identification method is a key inspection technique for automated ship loaders. In this paper, a fast ship identification algorithm was formulated based on the 3D point cloud of the ship, as generated by the Laser Measurement Systems (LMS) mounted on the ship loader. To meet the requirement of real-time computing for the automated ship loader, the 3D point cloud was first processed to reduce its dimensions from 3D point cloud into a 2D image. A projection method was then applied to locate and identify all bulk cargo holds in the ship. Finally, a group of experiments on ship identification was conducted using this algorithm in the Coal Terminal of Tianjin Port. The results showed that the computing time for a whole ship was lower than 200 ms and the error of the algorithm was lower than 10%, meeting the requirement of automated ship loaders.

Research on a Fast Human-Detection Algorithm for Unmanned Surveillance Area in Bulk Ports

With the development of port automation, most operational fields utilizing heavy equipment have gradually become unmanned. It is therefore imperative to monitor these fields in an effective and real-time manner. In this paper, a fast human-detection algorithm is proposed based on image processing. To speed up the detection process, the optimized histograms of oriented gradients (HOG) algorithm that can avoid the large number of double calculations of the original HOG and ignore insignificant features is used to describe the contour of the human body in real time. Based on the HOG features, using a training sample set consisting of scene images of a bulk port, a support vector machine (SVM) classifier combined with the AdaBoost classifier is trained to detect human. Finally, the results of the human detection experiments on Tianjin Port show that the accuracy of the proposed optimized algorithm has roughly the same accuracy as a traditional algorithm, while the proposed algorithm only takes 1/7 the amount of time. The accuracy and computing time of the proposed fast human-detection algorithm were verified to meet the security requirements of unmanned port areas.

항만 시스템의 자동화를 위한 시뮬레이션 방법론 : 컨테이너 터미널의 예

A simulation technique is very useful method to analyze the performance on various engineering area. To automate port systems, we have need of simulation to analyze an effect of assigning and operating devices. Thus we propose simulation methodology to be applied to an analysis, evaluation, planning for port automation. To do this, we have adopted the discrete event system specification based system entity structure / model base framework for modeling and simulation environment. We have performed modeling and simulation on entities of port systems such as container crane, yard tractor, transfer crane, etc. The proposed methodology has an advantage being able to effectively simulate on alternatives of composition and operation strategy for port systems. Some case studies will show the validity of proposed simulation methodology.

PC-based off-line programming in the shipbuilding industry: open architecture

In this paper, a PC-based off-line programming (OLP) method using the virtual reality modeling language is proposed. The developed OLP system consists of simulation programs, a block-arrangement algorithm, an optimal traveling-path generation algorithm, an automatic robot program generator and the TRIBON CAD interface. The strength of the developed PC-based OLP system lies in its flexibility in handling the changes in the robot's target objects. The operator can generate robot programs very easily and quickly. Possible applications of the developed OLP can be extended to port automation, container loading/unloading processes as well as painting and grinding processes in the shipbuilding industry.

An adaptive control of an autonomous guided vehicle system using cell-mediated immune algorithm controller and vision sensor

In this paper, we proposed an adaptive control method which is named CMIA (cell-mediated immune algorithm) controller with PID scheme. It is based on specific immune response of the biological immune system which is the cell-mediated immune response. It is also applied for the autonomous guided vehicle (AGV) system which is manufactured in this paper. The AGV is used for the port automation to carry container without human and to overcome uncertainty and nonlinearity because of running in the outdoor. To verify the performance of the proposed CMIA controller, some experiments for the AGV system are performed. Finally, the experimental results for the control of steering and speed of an AGV system illustrate the effectiveness of the proposed control scheme. These results for the proposed method also show that it has better performance than other conventional controller design methods.

면역시스템에 기반한 적응제어기 설계에 관한 연구

In this paper, we proposed two types of adaptive control mechanism which is named HIA(Humoral Immune Algorithm) PID and CMIA(Cell-Mediated Immune Algorithm) controller based on biological immune system under engineering point of view. The HIA PID which has real time control scheme is focused on the humoral immunity and the latter which has the self-tuning mechanism is focused on the T-cell regulated immune response. To verify the performance of the proposed controller, some experiments for the control of AGV which is used for the port automation to carry container without human are performed. The experimental results for the control of steering and speed of an AGV system illustrate the effectiveness of the proposed control scheme. Moreover, in that results, proposed controllers have better performance than other conventional PID controller and intelligent control method which is the NN(neural network) PID controller.